This artificial intelligence model mimics human brain

New York, July 24 (IANS) A team of US researchers has developed an "artificial synapse" that does not process information like a digital computer but rather mimics the way human brain completes tasks.

The discovery can lead to energy-efficient AI devices that would revolutionise our lives, said the team from University of Pittsburgh.

"The analog nature and massive parallelism of the brain are partly why humans can outperform even the most powerful computers when it comes to higher order cognitive functions such as voice recognition or pattern recognition in complex and varied data sets," explained Dr Feng Xiong, Assistant Professor of Electrical and Computer Engineering.

An emerging field called "neuromorphic computing" focuses on the design of computational hardware inspired by human brain.

Dr Xiong and his team built graphene-based "artificial synapses" in a 2D honeycomb configuration of carbon atoms.

Graphene's conductive properties allowed the researchers to finely tune its electrical conductance. The graphene synapse demonstrated excellent energy efficiency just like biological synapses, said the study published in the journal Advanced Materials.

In the recent resurgence of AI, computers can replicate the brain in certain ways but it takes about a dozen digital devices to mimic one analog synapse.

The human brain has hundreds of trillions of synapses for transmitting information, so building a brain with digital devices is seemingly impossible, or at the very least, not scalable.

The graphene-based neural networks can be employed in flexible and wearable electronics to enable computation at the "edge of the Internet" - places where computing devices such as sensors make contact with the physical world.

The development of an artificial brain that functions like the analog human brain still requires a number of breakthroughs, said researchers.

There is a need to find the right configurations to optimise these new "artificial synapses".

Despite the challenges, Dr Xiong said he's optimistic about the direction they're headed.

"We are excited about this progress since it can potentially lead to the energy-efficient, hardware implementation of neuromorphic computing, which is currently carried out in power-intensive GPU clusters," he noted.

--IANS

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